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DBMS > Apache IoTDB vs. Microsoft Access vs. Solr vs. Sphinx

System Properties Comparison Apache IoTDB vs. Microsoft Access vs. Solr vs. Sphinx

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonSolr  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)A widely used distributed, scalable search engine based on Apache LuceneOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMSSearch engineSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score101.16
Rank#11  Overall
#8  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Score5.95
Rank#55  Overall
#5  Search engines
Websiteiotdb.apache.orgwww.microsoft.com/­en-us/­microsoft-365/­accesssolr.apache.orgsphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldeveloper.microsoft.com/­en-us/­accesssolr.apache.org/­resources.htmlsphinxsearch.com/­docs
DeveloperApache Software FoundationMicrosoftApache Software FoundationSphinx Technologies Inc.
Initial release2018199220062001
Current release1.1.0, April 20231902 (16.0.11328.20222), March 20199.6.1, May 20243.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infoBundled with Microsoft OfficeOpen Source infoApache Version 2Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++JavaC++
Server operating systemsAll OS with a Java VM (>= 1.8)Windows infoNot a real database server, but making use of DLLsAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingno
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.noyes
Secondary indexesyesyesyes infoAll search fields are automatically indexedyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageyes infobut not compliant to any SQL standardSolr Parallel SQL InterfaceSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
ADO.NET
DAO
ODBC
OLE DB
Java API
RESTful HTTP/JSON API
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesyes infosince Access 2010 using the ACE-engineJava pluginsno
Triggersyesyes infosince Access 2010 using the ACE-engineyes infoUser configurable commands triggered on index changesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infobut no files for transaction loggingoptimistic lockingno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infobut no files for transaction loggingyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlyesno infoa simple user-level security was built in till version Access 2003yesno

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More resources
Apache IoTDBMicrosoft AccessSolrSphinx
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